Difference in Cd accumulation among varieties with different growth duration corresponding to typical agro-climate condition in rice ratooning system.

Introduction: The ratoon rice planting area is gradually expanding, but there has been relatively little research on ratoon rice grains contaminated with Cd. Methods: In this study, five ratoon rice varieties were selected and divided into three groups according to early-maturity (growth duration: 100-110 days), mid-maturity (growth duration: 110-120 days) and late-maturity (growth duration: 120-130 days) varieties. Field experiments were done to study the differences in Cd accumulation among ratoon rice varieties with different growth duration. Results: The results showed that the Cd accumulation and concentration of grains spikelet at each growth stage in the main crop were in the order of late-maturity > mid-maturity > early-maturity varieties. However, the trends in Cd concentration and accumulation in grains spikelet during the ratoon crop were the opposite. Analysis found that as the growth duration of the variety extended, the accumulated temperature and daily average temperature in the main crop increased, which significantly increased the translocation factors of Cd from root, stem, and leaf to grains spikelet, and increased the daily average Cd accumulation rate in grains spikelet. The daily average temperature in the ratoon crop increased as the growth duration shortened. The early-maturity variety had higher Cd accumulation in stubble, which promoted the translocation of Cd from the root, stem, and leaf of the plant to the grains spikelet. Discussion: Therefore, appropriately shortening the growth duration of the main crop and extending the growth duration of the ratoon crop are important ways to reduce Cd accumulation in ratoon rice in areas with mild Cd pollution.

[Characteristics and Cause of PM2.5 During Haze Pollution in Winter 2022 in Zhoukou, China].

The characteristics and main factors of causes of haze in Zhoukou in January 2022 were analyzed. Six air pollutants, water-soluble ions, elements, OC, EC, and other parameters in fine particulate matter were monitored and analyzed using a set of online high-time-resolution instruments in an urban area. The results showed that the secondary inorganic aerosols(SNA), carbonaceous aerosols(CA, including organic carbon OC and inorganic carbon EC), and reconstructed crustal materials(CM, such as Al2O3, SiO2, CaO, and Fe2O3, etc.) were the three main components, accounting for 61.3%, 24.3%, and 9.72% in PM2.5, respectively. The concentrations of SNA, CA, CM, and SOA were increased, accompanied with higher AQI. The sulfur oxidation rate(SOR) and nitrogen oxidation rate(NOR) in January were 0.53 and 0.46, respectively. The growth rates[µg·(m3·h)] of sulfate and nitrate were 0.027(-5.89-9.47, range) and 0.051(-23.1-12.4), respectively. During the haze period, the growth rates of sulfate and nitrate were 0.13 µg·(m3·h)-1and 0.24 µg·(m3·h)-1, which were 4.8 and 4.7 times higher than the average value of January, respectively. Although the sulfur oxidation rate was greater than the nitrogen oxidation rate, the growth rate of nitrate was approximately 1.8 times that of sulfate owing to the difference in the concentration of gaseous precursors and the influence of relative humidity. The growth rates of nitrate in SNA were significantly higher than those of sulfate on heavily polluted days. The values of SOR, NOR, and concentrations of SNA and SOA during higher AQI and humidity periods were higher than those in lower AQI and humidity periods. The Ox(NO2+O3) decreased with the increase in relative humidity. The SOA was higher at nighttime, increasing faster with the humidity than that in daytime. Under the situation of lower temperature, higher humidity, and lower wind speed, the emission of gaseous precursors of SNA requires further attention in Zhoukou in winter. Advanced control strategies of emissions of SO2 and NO2, such as mobile sources and coal-burning sources, could reduce the peak of haze in winter efficiently.

Preparation of Ir-Cu/C nanosheets for the oxygen evolution reaction by room temperature plasma carbonization.

Ir-Cu/C nanosheets with a thickness of about 2 nm were prepared using Ar plasma carbonization and reduction at room temperature. The obtained Ir-Cu/C catalyst, composed of single atom Ir-doped Cu nanoparticles embedded in a carbon framework, exhibits efficient oxygen evolution reaction activity with a low overpotential.

Assessment of the operation status of electronic prescription at community pharmacies in Chengdu, China: a simulated patient study.

BACKGROUND: Information and technologies relevant to eHealth have developed rapidly over the past two decades. Based on this, China piloted "Internet + " pattern and some regions piloted electronic prescription services to explore telepharmacy services. OBJECTIVE: To describe the processes and assess the operation status of electronic prescription services mode for community pharmacies in China. METHODS: The simulated patient methodology was used to conduct a cross-sectional study in 317 community pharmacies from six districts in Chengdu, China in 2019. Simulated patients expressed three levels of service demands based on scenario about acute upper respiratory tract infections to evaluate the recommendation strength of electronic prescription services and telepharmacy service in community pharmacies. The descriptive statistics was completed to obtain the characteristics of the visit process, student t-test and χ2 test (P < 0.05 was considered statistically significant) were used for inferential statistical analysis to determine differences in characteristics and degree of recommendation between pharmacies. RESULTS: Three Hundred Seventeen record sheets were effectively collected. The third-party platform was recommended in 195 (61.5%) interactions. The main reason for not recommending is non-prescription dispensing of prescription drugs (27.1%). 90.3% interactions waited less than 1 min, the counseling duration was less than 5 min in all interactions, and most community pharmacies had good network conditions (81.5%). 97.4% remote physicians offered professional counseling, only 22.1% of the pharmacists provided medication advice. CONCLUSIONS: The electronic prescription services mode for community pharmacies in Chengdu provides a convenient drug purchase process but remains some problems. For example, prescribing drugs without a prescription and services provided by pharmacists was poor, etc. The relevant supporting policies should be improved in future development process.

Independent and Combined Associations of Blood Manganese, Cadmium and Lead Exposures with the Systemic Immune-Inflammation Index in Adults.

Manganese (Mn), cadmium (Cd) and lead (Pb) have toxic effects on the immune system. However, their independent and combined effects on immune-inflammation responses are unclear. In recent years, the systemic immune-inflammation index (SII) has been developed as an integrated and novel inflammatory indicator. A retrospective cross-sectional study of 2174 adults ≥20 years old from the National Health and Nutrition Examination Survey (NHANES) 2015-2016 was conducted. Generalized linear models were used to evaluate the independent and combined associations of SII with blood Mn, Cd and Pb levels. As continuous variables, both blood Cd and Mn showed dose-dependent relationships with the SII before and after adjusting for all potential confounding factors. Metal concentrations were then converted into categorical variables. Compared with the adults in the lowest Cd or Mn tertile, those in the highest tertile had higher risks of elevated SII. Furthermore, co-exposure to Mn and Cd also showed a positive relationship with the SII after adjusting for all confounding factors. However, the single effect of Pb exposure and the joint effect of Pb and other metal exposures on the SII were not observed. This study provides important epidemiological evidence of the associations of SII with single and co-exposure effects of blood Mn, Cd, and Pb.

Effects of vegetation restoration on soil properties and vegetation attributes in the arid and semi-arid regions of China.

Driven by the goal of reversing desertification and recovering degraded lands, a wide range of vegetation restoration practices (such as planting and fencing) have been implemented in China's drylands. It is essential to examine the effects of vegetation restoration and environmental factors on soil nutrients to optimize restoration approaches. However, quantitative evaluation on this topic is insufficient due to a lack of long-term field monitoring data. This study evaluated the effects of sandy steppe restoration and sand dune fixation in the semi-arid desert, and natural and artificial vegetation restoration in the arid desert. It considered soil and plant characteristics using long-term (2005-2015) data from the Naiman Research Station located in the semi-arid region and Shapotou Research Station in the arid region of China's drylands. Results showed the sandy steppe had higher soil nutrient contents, vegetation biomass and rate of accumulating soil organic matter (OM) than the fixed dunes and moving dunes. Soil nutrient contents and vegetation biomass of the natural vegetation of Artemisia ordosica were higher than those of the artificial restoration of Artemisia ordosica since 1956. Artificial restoration had a higher rate of accumulating soil OM, total nitrogen (TN) and grass litter biomass than natural restoration. Soil water indirectly affected soil OM by affecting vegetation. Grass diversity was the main influencing factor on soil OM variance in the semi-arid Naiman desert while shrub diversity was the main factor in the arid Shapotou desert. These findings indicate that sand fixation in the semi-arid desert and vegetation restoration in the arid desert bring benefits for soil nutrient accumulation and vegetation improvement, and that natural restoration is preferable to artificial restoration. Results can be used to formulate sustainable vegetation restoration strategies, such as encouraging natural restoration, considering local resource constraints, and giving priority to restoring shrubs in arid areas with limited water.

Effects of grazing and enclosure management on soil physical and chemical properties vary with aridity in China's drylands.

Dryland soils are nutrient-poor and prone to degradation due to aridity, grazing and enclosure. It is essential to examine the effects of grazing and enclosure on aridity-induced soil degradation in dryland ecosystems to optimize land management practices in response to climate change. However, quantitative evaluation on this topic is scarce due to a lack of long-term field monitoring data. This study evaluated the combined effects of aridity and grazing/enclosure using long-term data (2005-2015) from three research stations on soil physical and chemical properties in typical steppes and desert steppes across the semi-arid and hyper-arid areas of China's drylands. Results showed that soil organic matter (OM) content was higher for enclosures (20.50 g/kg) than for grazing (19.06 g/kg). In the semi-arid steppe, enclosures aged 30-33 years had the highest soil total nitrogen (TN) content (1.21 g/kg). Longer enclosures aged 34-36 years showed decreased soil TN content (0.88 g/kg). In the desert steppe, enclosures aged 5-8 years exhibited the highest soil OM (2.44 g/kg) and TN (0.21 g/kg) contents. Grazing enhanced the decrease of OM content (from 4.57 to 2.39 g/kg) with increasing aridity (1 - aridity index) from 0.35 to 1. These findings indicate that enclosures can improve soil fertility, but prolonged enclosures may have negative effects. Grazing had a synergistic effect on the decrease of OM with aridity. Results can be used in response to climate changes to formulate sustainable land management strategies, such as reducing the enclosure period in wetter and restored areas, and diminishing the grazing intensity in areas with higher aridity.

Smoothed Quantile Regression with Large-Scale Inference.

Quantile regression is a powerful tool for learning the relationship between a response variable and a multivariate predictor while exploring heterogeneous effects. This paper focuses on statistical inference for quantile regression in the "increasing dimension" regime. We provide a comprehensive analysis of a convolution smoothed approach that achieves adequate approximation to computation and inference for quantile regression. This method, which we refer to as conquer, turns the non-differentiable check function into a twice-differentiable, convex and locally strongly convex surrogate, which admits fast and scalable gradient-based algorithms to perform optimization, and multiplier bootstrap for statistical inference. Theoretically, we establish explicit non-asymptotic bounds on estimation and Bahadur-Kiefer linearization errors, from which we show that the asymptotic normality of the conquer estimator holds under a weaker requirement on dimensionality than needed for conventional quantile regression. The validity of multiplier bootstrap is also provided. Numerical studies confirm conquer as a practical and reliable approach to large-scale inference for quantile regression. Software implementing the methodology is available in the R package conquer.

Silence is golden? Relationship between silent behavior among online community members and operation performance from the perspective of personality trait.

As companies are transforming their branding, marketing, operations, and research and development (R&D) by running online communities to build their core competitive advantages in the digital era, the silent majority is still the norm in the online community and has become the focus of online community operations. Thus, it has become the core issue that why silent behavior of online community members occurs and its impact on operation performance of the online community. According to the traditional theory of organizational behavior, this study focuses on the theoretical model of the relationship between proactive personality, silent behavior of online community members (acquiescent, defensive, and prosocial silence), and operation performance of the online community, and further analyzes the impact of community identification on these relationships. Eight hundred online community members in China participated in this study. The results indicate that: (1) proactive personality has a significant negative impact on acquiescent silent and defensive silent behavior of the online community members, and a significant positive impact on prosocial silent behavior of the online community members; (2) The acquiescent silence and defensive silence have a significant negative impact on online community operation performance, whereas prosocial silence has a significant positive impact on community operation performance; (3) The acquiescent silence and defensive silence have a significant mediating effect on the relationship between proactive personality and community operation performance; (4) Online community identification has a moderating effect on the relationship between silent behavior and online community operation performance. The study proposes the mechanisms and double-edged sword effects of the silent behavior of online community members from the perspective of personality traits. On the one hand, it generalizes the research of traditional organizational silent behavior theory to the context of the online community. On the other hand, it provides reference and inspiration for the theoretical research and practical management of silent behavior of online community members.

An improved method of using two-dimensional model to evaluate the carrying capacity of regional water resource in Inner Mongolia of China.

The evaluation of regional water resource carrying capacity has been repeatedly conducted to provide a scientific basis for the local water resource management and the sustainable development, in particular in the water-limited regions. However, the definition of regional water resource carrying capacity and its evaluation method are still arguable. Through a case study of Inner Mongolia, located in the arid and semi-arid northern China, this paper developed an improved method to calculate regional water resource carrying capacity by the combination of the water supply-demand analysis and the S-shaped curve threshold analysis. The spatial and temporal patterns of the regional water resource carrying capacity in Inner Mongolia during 2000-2019 was evaluated at three scales, namely the province scale, the basin scale and the city scale. The results showed that the average regional water resource carrying capacity of the whole province was 0.25 (the full mark is 1.00); at the basin scale, the Yellow River Basin had the lowest regional water resource carrying capacity (0.17) among all the basins, showing that the utilization of the water resources was unreasonable; at the city scale, the average regional water resource carrying capacities in Hulunbuir and Xilingol were both over 0.25, while those in Alxa, BayanNur and Wuhai were below 0.1; Hulunbuir had 25.48 billion m3 water surplus, while BayanNur suffered from an average water deficit of 4.51 billion m3 from 2000 to 2019. This paper has provided a reasonable way to measure the regional water resource carrying capacity using an improved method by incorporating S-shaped curve threshold analysis, which may have a wider application for the clustering and optimization of regional water management. In addition, the spatial and temporal patterns of regional water carrying capacity are beneficial for policymakers in the implementation of the effective water usage.

Research Note: Fine mapping of sequence variants associated with body weight of Lueyang black-boned chicken in the CCKAR gene.

Cholecystokinin A receptor (CCKAR) is a key receptor mediating satiety. Previous studies found that decreased expression of CCKAR attenuated satiety, and thus contributed to the high-growth of broiler chickens. The objective of this study is to map sequence variants associated with the growth of chickens in the CCKAR. The CCKAR and upstream 1.4 kb genomic sequences were resequenced to find out all sequence variants using 35 Lueyang black-boned chickens (LBC). Haplotypes were reconstructed using the PHASE program. Linkage disequilibrium between variants was analyzed using the Haploview software. Associations of 33 tag SNPs that captured 89% of all variants with body weight of LBC (n = 675) at 16 (BW16), 20 (BW20) weeks of age and the onset (BWOEP) of egg production were tested using linear mixed models. A total of 126 SNPs were found and formed 41 haplotypes in 35 resequenced samples. Average length of haplotype blocks is 129 bp, indicating that LBC maintains low linkage disequilibrium at the CCKAR locus. Eleven of 33 tag SNPs were significantly associated with BW16, but not with BW20 and BWOEP. These significantly associated variants were most (8/11) distributed in a 2 kb region (chr4:73206169-73208244) around the Exon3. They together with 33 captured variants potentially disrupted binding sites of 471 transcription factors. Twelve variants can disrupt appetite (FOXO1) or lipid metabolism-related TF (AR and C/EBP) motifs. This study recognized chr4:73206169-73208244 as a key region harboring functional variants affecting the growth of chickens.

Adaptive Huber Regression.

Big data can easily be contaminated by outliers or contain variables with heavy-tailed distributions, which makes many conventional methods inadequate. To address this challenge, we propose the adaptive Huber regression for robust estimation and inference. The key observation is that the robustification parameter should adapt to the sample size, dimension and moments for optimal tradeoff between bias and robustness. Our theoretical framework deals with heavy-tailed distributions with bounded (1 + δ)-th moment for any δ > 0. We establish a sharp phase transition for robust estimation of regression parameters in both low and high dimensions: when δ ≥ 1, the estimator admits a sub-Gaussian-type deviation bound without sub-Gaussian assumptions on the data, while only a slower rate is available in the regime 0 < δ < 1 and the transition is smooth and optimal. In addition, we extend the methodology to allow both heavy-tailed predictors and observation noise. Simulation studies lend further support to the theory. In a genetic study of cancer cell lines that exhibit heavy-tailedness, the proposed methods are shown to be more robust and predictive.

FarmTest: Factor-adjusted robust multiple testing with approximate false discovery control.

Large-scale multiple testing with correlated and heavy-tailed data arises in a wide range of research areas from genomics, medical imaging to finance. Conventional methods for estimating the false discovery proportion (FDP) often ignore the effect of heavy-tailedness and the dependence structure among test statistics, and thus may lead to inefficient or even inconsistent estimation. Also, the commonly imposed joint normality assumption is arguably too stringent for many applications. To address these challenges, in this paper we propose a Factor-Adjusted Robust Multiple Testing (FarmTest) procedure for large-scale simultaneous inference with control of the false discovery proportion. We demonstrate that robust factor adjustments are extremely important in both controlling the FDP and improving the power. We identify general conditions under which the proposed method produces consistent estimate of the FDP. As a byproduct that is of independent interest, we establish an exponential-type deviation inequality for a robust U-type covariance estimator under the spectral norm. Extensive numerical experiments demonstrate the advantage of the proposed method over several state-of-the-art methods especially when the data are generated from heavy-tailed distributions. The proposed procedures are implemented in the R-package FarmTest.

A NEW PERSPECTIVE ON ROBUST M-ESTIMATION: FINITE SAMPLE THEORY AND APPLICATIONS TO DEPENDENCE-ADJUSTED MULTIPLE TESTING.

Heavy-tailed errors impair the accuracy of the least squares estimate, which can be spoiled by a single grossly outlying observation. As argued in the seminal work of Peter Huber in 1973 [Ann. Statist.1 (1973) 799-821], robust alternatives to the method of least squares are sorely needed. To achieve robustness against heavy-tailed sampling distributions, we revisit the Huber estimator from a new perspective by letting the tuning parameter involved diverge with the sample size. In this paper, we develop nonasymptotic concentration results for such an adaptive Huber estimator, namely, the Huber estimator with the tuning parameter adapted to sample size, dimension, and the variance of the noise. Specifically, we obtain a sub-Gaussian-type deviation inequality and a nonasymptotic Bahadur representation when noise variables only have finite second moments. The nonasymptotic results further yield two conventional normal approximation results that are of independent interest, the Berry-Esseen inequality and Cramér-type moderate deviation. As an important application to large-scale simultaneous inference, we apply these robust normal approximation results to analyze a dependence-adjusted multiple testing procedure for moderately heavy-tailed data. It is shown that the robust dependence-adjusted procedure asymptotically controls the overall false discovery proportion at the nominal level under mild moment conditions. Thorough numerical results on both simulated and real datasets are also provided to back up our theory.

ARE DISCOVERIES SPURIOUS? DISTRIBUTIONS OF MAXIMUM SPURIOUS CORRELATIONS AND THEIR APPLICATIONS.

Over the last two decades, many exciting variable selection methods have been developed for finding a small group of covariates that are associated with the response from a large pool. Can the discoveries by such data mining approaches be spurious due to high dimensionality and limited sample size? Can our fundamental assumptions on exogeneity of covariates needed for such variable selection be validated with the data? To answer these questions, we need to derive the distributions of the maximum spurious correlations given certain number of predictors, namely, the distribution of the correlation of a response variable Y with the best s linear combinations of p covariates X, even when X and Y are independent. When the covariance matrix of X possesses the restricted eigenvalue property, we derive such distributions for both finite s and diverging s, using Gaussian approximation and empirical process techniques. However, such a distribution depends on the unknown covariance matrix of X. Hence, we use the multiplier bootstrap procedure to approximate the unknown distributions and establish the consistency of such a simple bootstrap approach. The results are further extended to the situation where residuals are from regularized fits. Our approach is then applied to construct the upper confidence limit for the maximum spurious correlation and testing exogeneity of covariates. The former provides a baseline for guarding against false discoveries due to data mining and the latter tests whether our fundamental assumptions for high-dimensional model selection are statistically valid. Our techniques and results are illustrated by both numerical examples and real data analysis.

A high-capacity dual core-shell structured MWCNTs@S@PPy nanocomposite anode for advanced aqueous rechargeable lithium batteries.

Anode materials with high capacity for aqueous rechargeable lithium batteries (ARLBs) are very rarely reported. Here we found that a dual core-shell structured MWCNTs@S@PPy nanocomposite prepared by us shows excellent electrochemical performance. Its initial discharge capacity in a saturated LiAc aqueous electrolyte is very high, which is up to 481 mA h g-1 based on the weight of the composite and 879 mA h g-1 based on the sulfur content. It shows excellent rate capability. When nanotube LiMn2O4 is used as a cathode, the assembled ARLB can deliver an energy density of 110 Wh kg-1 based on two electrodes and show excellent cycling. These results show great promise for the practical application of ARLBs.

Simulation-based hypothesis testing of high dimensional means under covariance heterogeneity.

In this article, we study the problem of testing the mean vectors of high dimensional data in both one-sample and two-sample cases. The proposed testing procedures employ maximum-type statistics and the parametric bootstrap techniques to compute the critical values. Different from the existing tests that heavily rely on the structural conditions on the unknown covariance matrices, the proposed tests allow general covariance structures of the data and therefore enjoy wide scope of applicability in practice. To enhance powers of the tests against sparse alternatives, we further propose two-step procedures with a preliminary feature screening step. Theoretical properties of the proposed tests are investigated. Through extensive numerical experiments on synthetic data sets and an human acute lymphoblastic leukemia gene expression data set, we illustrate the performance of the new tests and how they may provide assistance on detecting disease-associated gene-sets. The proposed methods have been implemented in an R-package HDtest and are available on CRAN.

Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering.

Comparing large covariance matrices has important applications in modern genomics, where scientists are often interested in understanding whether relationships (e.g., dependencies or co-regulations) among a large number of genes vary between different biological states. We propose a computationally fast procedure for testing the equality of two large covariance matrices when the dimensions of the covariance matrices are much larger than the sample sizes. A distinguishing feature of the new procedure is that it imposes no structural assumptions on the unknown covariance matrices. Hence, the test is robust with respect to various complex dependence structures that frequently arise in genomics. We prove that the proposed procedure is asymptotically valid under weak moment conditions. As an interesting application, we derive a new gene clustering algorithm which shares the same nice property of avoiding restrictive structural assumptions for high-dimensional genomics data. Using an asthma gene expression dataset, we illustrate how the new test helps compare the covariance matrices of the genes across different gene sets/pathways between the disease group and the control group, and how the gene clustering algorithm provides new insights on the way gene clustering patterns differ between the two groups. The proposed methods have been implemented in an R-package HDtest and are available on CRAN.

Three-Dimensional Carbon Nanotubes Forest/Carbon Cloth as an Efficient Electrode for Lithium-Polysulfide Batteries.

The development of a three-dimensionally flexible, large-surface area, high-conductivity electrode is important to improve the low conductivity and utilization of active materials and restrict the shuttle of long-chain polysulfides in Li-polysulfide batteries. Herein, we constructed an integrated three-dimensional carbon nanotube forest/carbon cloth electrode with heteroatom doping and high electrical conductivity. The as-constructed electrode provides strong trapping on the polysulfide species and fast charge transfer. Therefore, the Li-polysulfide batteries with as-constructed electrodes achieved high specific capacities of ∼1200 and ∼800 mA h g-1 at 0.1 and 1 C, respectively. After 300 cycles at 0.5 C, a specific capacity of 623 mA h g-1 was retained.

Guarding against Spurious Discoveries in High Dimensions.

Many data-mining and statistical machine learning algorithms have been developed to select a subset of covariates to associate with a response variable. Spurious discoveries can easily arise in high-dimensional data analysis due to enormous possibilities of such selections. How can we know statistically our discoveries better than those by chance? In this paper, we define a measure of goodness of spurious fit, which shows how good a response variable can be fitted by an optimally selected subset of covariates under the null model, and propose a simple and effective LAMM algorithm to compute it. It coincides with the maximum spurious correlation for linear models and can be regarded as a generalized maximum spurious correlation. We derive the asymptotic distribution of such goodness of spurious fit for generalized linear models and L1-regression. Such an asymptotic distribution depends on the sample size, ambient dimension, the number of variables used in the fit, and the covariance information. It can be consistently estimated by multiplier bootstrapping and used as a benchmark to guard against spurious discoveries. It can also be applied to model selection, which considers only candidate models with goodness of fits better than those by spurious fits. The theory and method are convincingly illustrated by simulated examples and an application to the binary outcomes from German Neuroblastoma Trials.